The dataset of landuse types in Qilian Mountains National Park in 1985 is a vector dataset based on the remote sensing monitoring dataset of the current landuse situation in China by CAS, which is obtained through cropping and splicing operations. The data production production is vector data generated by manual visual interpretation using Landsat TM/ETM remote sensing images as the main data source. 3 datasets for 2000-2020 are raster datasets with 30m resolution based on GlobeLand30 global 30m ground cover data, obtained through mask extraction and other operations. The land use types of all datasets include 10 primary types of cropland, forest, shrubland, grassland, wetland, water, tundra, impervious surface, bareland, glacier, and permanent snow. The data products can detect most of the land cover changes caused by human activities, which is very important in practical applications. This data can be used to analyze the historical land use types in the Qilian Mountains region and to analyze the changes of land use types in the Qilian Mountains region in combination with the current landuse type data.
NIAN Yanyun
This data is the debris flow risk assessment data obtained from the analysis and Research on the debris flow disaster in the China Pakistan Economic Corridor, and the data source is the risk and vulnerability analysis results obtained from this study; The research method is based on the risk expression given by the United Nations Department of Humanitarian Affairs (1992): risk = hazard × Vulnerability, risk analysis of debris flow disaster in the study area.. The purpose of this data is to assess the risk of debris flow disaster in the China Pakistan Economic Corridor, understand the relationship between the intensity of major debris flow risk, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.
SU Fenghuan
This data is the debris flow risk assessment data obtained from the analysis and Research on the debris flow disaster in the China Pakistan Economic Corridor, and the data source is the risk and vulnerability analysis results obtained from this study; The research method is based on the risk expression given by the United Nations Department of Humanitarian Affairs (1992): risk = hazard × Vulnerability, risk analysis of debris flow disaster in the study area.. The purpose of this data is to assess the risk of debris flow disaster in the China Pakistan Economic Corridor, understand the relationship between the intensity of major debris flow risk, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.
SU Fenghuan
This data is the debris flow risk assessment data, which is obtained from the analysis and research of the debris flow disaster in the China Pakistan Economic Corridor. The sample data of debris flow is the detailed data of debris flow disaster through remote sensing interpretation and on-site verification. A risk assessment system is established to evaluate the debris flow risk in the study area by using the information method, and then the risk area is divided by using the natural breakpoint method. This data can be used to assess the risk of major debris flow disasters, understand the relationship between the risk degree of major debris flow, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.
SU Fenghuan
Net Primary Productivity (NPP) refers to the total amount of organic matter produced by photosynthesis in green plants per unit time and area. As the basis of water cycle, nutrient cycle and biodiversity change in terrestrial ecosystems, NPP is an important ecological indicator for estimating earth support capacity and evaluating sustainable development of terrestrial ecosystems. This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NPP products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Leaf Area Index (LAI) is defined as half of the total Leaf Area within the unit projected surface Area, and is one of the core parameters used to describe vegetation. LAI controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, carbon cycle and precipitation interception, and meanwhile provides quantitative information for the initial energy exchange on the surface of vegetation canopy. LAI is a very important parameter to study the structure and function of vegetation ecosystem. This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Different forms of precipitation (snow, sleet, and rain) have divergent effects on the Earth’s surface water and energy fluxes. Therefore, discriminating between these forms is of significant importance, especially under a changing climate. We applied a state-of-the-art parameterization scheme with wet-bulb temperature, relative humidity, surface air pressure, and elevation as inputs, as well as observational gridded datasets with a maximum spatial resolution of 0.25◦, to generate a gridded dataset of different forms of daily precipitation (snow, sleet, and rain) and their temperature threshold across mainland China from 1961-2016. The annual snow, sleet, and rain amount were further calculated. The dataset may benefit various research communities, such as cryosphere science, hydrology, ecology, and climate change.
SU Bo , ZHAO Hongyu
Mountain glaciers are important freshwater resources in Western China and its surrounding areas. It is at the drainage basin scale that mountain glaciers provide meltwater that humans exploit and utilize. Therefore, the determination of glacierized river basins is the basis for the research on glacier meltwater provisioning functions and their services. Based on the Randolph glacier inventory 6.0, Chinese Glacier Inventories, China's river basin classifications (collected from the Data Centre for Resources and Environmental Sciences, Chinese Academy of Sciences), and global-scale HydroBASINS (www.hydrosheds.org), the following dataset was generated by the intersection between river basins and glacier inventory: (1) Chinese glacierized macroscale and microscale river basins; (2) International glacierized macroscale river basin fed by China’s glaciers; (3) Glacierized macroscale river basin data across High Mountain Asia. This data takes the common river basin boundaries in China and the globe into account, which is poised to provide basic data for the study of historical and future glacier water resources in China and its surrounding areas.
SU Bo
The Second Tibetan Plateau Scientific Expedition and Research Task V Theme III "Conservation and Sustainable Utilization of Plateau Microbial Diversity" (2019QZKK0503) carried out more than 30 field scientific expeditions in the first and second years. Footprints cover most of the Tibetan Plateau, including the investigation of glaciers (such as Qiangyong Glacier, Tanggula Glacier, Everest East Rongbu glacier, Jiemayangzong Glacier, Palung 4 Glacier, etc.), lakes, soils, fungi, lichens, animals in Southeast Tibet, Qiangtang Plateau, Cocosili and Himalayan region. The dataset contains 6,471 photos and videos, including habitat photos, working photos, and scientific images collected during the first and second years of fieldwork.
LIU Yongqin
This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from July 22 to September 5 in 2021. The site (100.376° E, 38.853°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 3 observation samples, each of which is about 30m×30m in size, and the latitude and longitude are (100.374°E, 38.855°N), (100.371° E, 38.854°N), (100.369°E, 38.854°N). Four sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 5 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin, CHE Tao, Qu Yonghua, XU Ziwei, TAN Junlei
The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream of Heihe integrated observatory network from May 2 to December 26, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, CHE Tao, XU Ziwei, REN Zhiguo
The dataset contains the phenological camera observation data of the Daman Superstation in the midstream of Heihe integrated observatory network from January 1 to December 31, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, CHE Tao, XU Ziwei, TAN Junlei, REN Zhiguo
The dataset contains the phenological camera observation data of the Arou Superstation in the midstream of Heihe integrated observatory network from January 1 to December 31, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, CHE Tao, XU Ziwei, ZHANG Yang
Forest is an important terrestrial ecosystem, accounting for about one-third of the total land area. It plays an important role in regulating climate, providing habitat for species, and maintaining global ecosystem balance. The dynamic change of the tree-canopy cover will affect the structure, composition, and function of the forest ecosystem. Landsat data were used to derive the 30-m tree-canopy cover dataset based on the machine learning method. The dataset of the rate of tree-canopy cover change in the Eastern Himalayas from 1990 to 2020 was generated using the annual tree-canopy cover data. The results show that the average tree-canopy cover in this region had increased from 40.67% (1990) to 43.43% (2020), an increase of 2.76%, indicating that the forests in the Eastern Himalayas improved in the past few decades.
WANG Chunling , WANG Jianbang , HE Zhuoyu , FENG Min
This data is the annual average runoff data from 1495 to 2018 of Khorog Hydrometric Station of gunte River, a tributary of Amu Darya River, reconstructed based on tree ring data. The data obtained from the tree ring hydrology research carried out by the Urumqi desert Meteorology Institute of the China Meteorological Administration and the Institute of water issues, hydropower and ecology of the National Academy of Sciences of Tajikistan can be used for scientific research such as water resources assessment and water conservancy projects in mountainous areas of Central Asia.
SHANG Huaming
Data content: groundwater temperature data of Nukus irrigation area from January 2021 to December 2021, unit: 0.1 ℃. Data source and processing method: this data is collected from the automatic groundwater monitoring station in Nukus irrigation area. Data quality description: this data is site data with a time resolution of 3 hours. Results and prospects of data application: combined with other meteorological and hydrological parameters, hydrogeological conditions, especially the recharge, runoff and discharge conditions of groundwater, can be further identified and studied to master the dynamic law of groundwater, so as to provide a scientific basis for the evaluation of groundwater resources, scientific management and the research and Prevention of environmental geological problems.
LIU Tie
Normalized Difference Vegetation Index (NDVI) is the sum of the reflectance values of the NIR band and the red band by the Difference ratio of the reflectance values of the NIR band and the red band. Vegetation index synthesis refers to the selection of the best representative of vegetation index within the appropriate synthesis cycle, and the synthesis of a vegetation index grid image with minimal influence on spatial resolution, atmospheric conditions, cloud conditions, observation geometry, and geometric accuracy and so on. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
This data is obtained through observation at Namucuo multi cycle comprehensive observation and research station of Chinese Academy of Sciences (2019) and Tibetan southeast alpine environment comprehensive observation and research station of Chinese Academy of Sciences (2021), including the earth atmosphere exchange flux or vertical gradient of species such as O3, NOx, HONO, H2O and HCHO. The time range is from April 28, 2019 to July 10, 2019 (Namuco station) and from May 2, 2021 to May 13, 2021 (Southeast Tibet station). The data consists of five documents. Documents 1-4 are the flux data and H2O vertical gradient, HONO vertical gradient and NO2 vertical gradient observed at Namuco station in 2019. Document 5 is the flux data observed at Southeast Tibet station in 2021. During the monitoring period, data was missing due to instrument status problems. This data has broad application prospects and can serve graduate students and scientists with backgrounds such as atmospheric science, climatology, and ecology.
YE Chunxiang
Forest change (including forest loss and gain) is a complex ecological process influenced by natural and human activities, and has important impacts on global material cycles and energy flows. Based on long-term tree-canopy cover (TCC) data, the Bi-temporal class-probabilities model was used to detect forest changes, and a dataset of forest change of the Natural Forest Conversion Program area in northeast China from 1986 to 2018 was obtained (spatial resolution 30 meters with a temporal resolution of 1 year). The method of stratified random sampling was used to select 1000 points in the reserve and visual interpretation was carried out to evaluate the accuracy of forest change. The results show that the accuracy of forest loss (producer's accuracy = 85.21%; user's accuracy = 84.26%) and forest restoration (producer's accuracy = 87.74%; user's accuracy = 88.31%) are both high, which can effectively reflect the forest change status of the protected area.
WANG Jianbang , HE Zhuoyu , WANG Chunling , FENG Min, PANG Yong, YU Tao , LI Xin
The data set is a three-dimensional lithospheric stress field model in the Sichuan-Yunnan region, which is constrained by GPS velocity field and focal mechanism solution. A 3D finite element model of regional lithospheric deformation is constructed by using the lithospheric structure fracture information in Sichuan-Yunnan region. The velocity boundary constraints of the model are given by integrating the regional GPS velocity published in the existing researches and the latest observation. At the same time, the stress field of the model is constrained by the focal mechanism solution of regional small and medium earthquakes and mantle convection. A comprehensive simulation model of current crustal deformation and stress field in Sichuan-Yunnan region is constructed. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
XIONG Xiong
